id stringlengths 14 16 | text stringlengths 13 2.7k | source stringlengths 57 178 |
|---|---|---|
0d6e7b01bb24-1 | load_schema()
Load the graph schema information.
query(query)
Query the graph.
update(query)
Update the graph.
__init__(source_file: Optional[str] = None, serialization: Optional[str] = 'ttl', query_endpoint: Optional[str] = None, update_endpoint: Optional[str] = None, standard: Optional[str] = 'rdf', local_copy: Optio... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.rdf_graph.RdfGraph.html |
6eef03a5a44e-0 | langchain.graphs.graph_document.GraphDocument¶
class langchain.graphs.graph_document.GraphDocument[source]¶
Bases: Serializable
Represents a graph document consisting of nodes and relationships.
nodes¶
A list of nodes in the graph.
Type
List[Node]
relationships¶
A list of relationships in the graph.
Type
List[Relations... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.GraphDocument.html |
6eef03a5a44e-1 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[boo... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.GraphDocument.html |
6eef03a5a44e-2 | A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classm... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.GraphDocument.html |
8513d83af50e-0 | langchain.graphs.falkordb_graph.FalkorDBGraph¶
class langchain.graphs.falkordb_graph.FalkorDBGraph(database: str, host: str = 'localhost', port: int = 6379, username: Optional[str] = None, password: Optional[str] = None, ssl: bool = False)[source]¶
FalkorDB wrapper for graph operations.
Security note: Make sure that th... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.falkordb_graph.FalkorDBGraph.html |
8513d83af50e-1 | Create a new FalkorDB graph wrapper instance.
add_graph_documents(graph_documents: List[GraphDocument], include_source: bool = False) → None[source]¶
Take GraphDocument as input as uses it to construct a graph.
query(query: str, params: dict = {}) → List[Dict[str, Any]][source]¶
Query FalkorDB database.
refresh_schema(... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.falkordb_graph.FalkorDBGraph.html |
f1caadfcdd6b-0 | langchain.graphs.neo4j_graph.Neo4jGraph¶
class langchain.graphs.neo4j_graph.Neo4jGraph(url: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None, database: str = 'neo4j')[source]¶
Neo4j wrapper for graph operations.
Security note: Make sure that the database connection uses credentialsth... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.neo4j_graph.Neo4jGraph.html |
f1caadfcdd6b-1 | Take GraphDocument as input as uses it to construct a graph.
query(query: str, params: dict = {}) → List[Dict[str, Any]][source]¶
Query Neo4j database.
refresh_schema() → None[source]¶
Refreshes the Neo4j graph schema information.
Examples using Neo4jGraph¶
Neo4j
Diffbot Graph Transformer
Neo4j DB QA chain | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.neo4j_graph.Neo4jGraph.html |
cc77fedc2afc-0 | langchain.graphs.networkx_graph.NetworkxEntityGraph¶
class langchain.graphs.networkx_graph.NetworkxEntityGraph(graph: Optional[Any] = None)[source]¶
Networkx wrapper for entity graph operations.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only include necessary perm... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.networkx_graph.NetworkxEntityGraph.html |
cc77fedc2afc-1 | draw_graphviz(**kwargs: Any) → None[source]¶
Provides better drawing
Usage in a jupyter notebook:
>>> from IPython.display import SVG
>>> self.draw_graphviz_svg(layout="dot", filename="web.svg")
>>> SVG('web.svg')
classmethod from_gml(gml_path: str) → NetworkxEntityGraph[source]¶
get_entity_knowledge(entity: str, depth... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.networkx_graph.NetworkxEntityGraph.html |
77fe050102bc-0 | langchain.graphs.memgraph_graph.MemgraphGraph¶
class langchain.graphs.memgraph_graph.MemgraphGraph(url: str, username: str, password: str, *, database: str = 'memgraph')[source]¶
Memgraph wrapper for graph operations.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.memgraph_graph.MemgraphGraph.html |
77fe050102bc-1 | Query Neo4j database.
refresh_schema() → None[source]¶
Refreshes the Memgraph graph schema information.
Examples using MemgraphGraph¶
Memgraph QA chain | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.memgraph_graph.MemgraphGraph.html |
6767b6c855b8-0 | langchain.graphs.graph_document.Relationship¶
class langchain.graphs.graph_document.Relationship[source]¶
Bases: Serializable
Represents a directed relationship between two nodes in a graph.
source¶
The source node of the relationship.
Type
Node
target¶
The target node of the relationship.
Type
Node
type¶
The type of t... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.Relationship.html |
6767b6c855b8-1 | the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[boo... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.Relationship.html |
6767b6c855b8-2 | A unique identifier for this class for serialization purposes.
The unique identifier is a list of strings that describes the path
to the object.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classm... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.Relationship.html |
4db7acbbd147-0 | langchain.graphs.neptune_graph.NeptuneQueryException¶
class langchain.graphs.neptune_graph.NeptuneQueryException(exception: Union[str, Dict])[source]¶
A class to handle queries that fail to execute | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.neptune_graph.NeptuneQueryException.html |
25ef9fd27952-0 | langchain.graphs.graph_store.GraphStore¶
class langchain.graphs.graph_store.GraphStore[source]¶
An abstract class wrapper for graph operations.
Attributes
get_schema
Returns the schema of the Graph database
get_structured_schema
Returns the schema of the Graph database
Methods
__init__()
add_graph_documents(graph_docum... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_store.GraphStore.html |
2220aa1b9347-0 | langchain.graphs.graph_document.Node¶
class langchain.graphs.graph_document.Node[source]¶
Bases: Serializable
Represents a node in a graph with associated properties.
id¶
A unique identifier for the node.
Type
Union[str, int]
type¶
The type or label of the node, default is “Node”.
Type
str
properties¶
Additional proper... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.Node.html |
2220aa1b9347-1 | deep – set to True to make a deep copy of the model
Returns
new model instance
dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, ex... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.Node.html |
2220aa1b9347-2 | The unique identifier is a list of strings that describes the path
to the object.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model¶
classmethod parse_obj(obj: Any) → Model¶
classmethod parse_raw(b: Uni... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.graph_document.Node.html |
964dfea21381-0 | langchain.graphs.networkx_graph.get_entities¶
langchain.graphs.networkx_graph.get_entities(entity_str: str) → List[str][source]¶
Extract entities from entity string. | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.networkx_graph.get_entities.html |
be0ddd3d8afd-0 | langchain.graphs.networkx_graph.parse_triples¶
langchain.graphs.networkx_graph.parse_triples(knowledge_str: str) → List[KnowledgeTriple][source]¶
Parse knowledge triples from the knowledge string. | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.networkx_graph.parse_triples.html |
0fa31c184a0f-0 | langchain.graphs.arangodb_graph.get_arangodb_client¶
langchain.graphs.arangodb_graph.get_arangodb_client(url: Optional[str] = None, dbname: Optional[str] = None, username: Optional[str] = None, password: Optional[str] = None) → Any[source]¶
Get the Arango DB client from credentials.
Parameters
url – Arango DB url. Can ... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.arangodb_graph.get_arangodb_client.html |
b7684ba0a1a4-0 | langchain.graphs.nebula_graph.NebulaGraph¶
class langchain.graphs.nebula_graph.NebulaGraph(space: str, username: str = 'root', password: str = 'nebula', address: str = '127.0.0.1', port: int = 9669, session_pool_size: int = 30)[source]¶
NebulaGraph wrapper for graph operations.
NebulaGraph inherits methods from Neo4jGr... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.nebula_graph.NebulaGraph.html |
b7684ba0a1a4-1 | Create a new NebulaGraph wrapper instance.
execute(query: str, params: Optional[dict] = None, retry: int = 0) → Any[source]¶
Query NebulaGraph database.
query(query: str, retry: int = 0) → Dict[str, Any][source]¶
refresh_schema() → None[source]¶
Refreshes the NebulaGraph schema information.
Examples using NebulaGraph¶
... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.nebula_graph.NebulaGraph.html |
42a6bca9ef73-0 | langchain.graphs.kuzu_graph.KuzuGraph¶
class langchain.graphs.kuzu_graph.KuzuGraph(db: Any, database: str = 'kuzu')[source]¶
Kùzu wrapper for graph operations.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only include necessary permissions.
Failure to do so may resul... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.kuzu_graph.KuzuGraph.html |
ff765c3a274d-0 | langchain.graphs.hugegraph.HugeGraph¶
class langchain.graphs.hugegraph.HugeGraph(username: str = 'default', password: str = 'default', address: str = '127.0.0.1', port: int = 8081, graph: str = 'hugegraph')[source]¶
HugeGraph wrapper for graph operations.
Security note: Make sure that the database connection uses crede... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.hugegraph.HugeGraph.html |
60d9c90a9acb-0 | langchain.graphs.arangodb_graph.ArangoGraph¶
class langchain.graphs.arangodb_graph.ArangoGraph(db: Any)[source]¶
ArangoDB wrapper for graph operations.
Security note: Make sure that the database connection uses credentialsthat are narrowly-scoped to only include necessary permissions.
Failure to do so may result in dat... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.arangodb_graph.ArangoGraph.html |
60d9c90a9acb-1 | Convenience constructor that builds Arango DB from credentials.
Parameters
url – Arango DB url. Can be passed in as named arg or set as environment
var ARANGODB_URL. Defaults to “http://localhost:8529”.
dbname – Arango DB name. Can be passed in as named arg or set as
environment var ARANGODB_DBNAME. Defaults to “_syste... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.arangodb_graph.ArangoGraph.html |
1a9522a35e73-0 | langchain.graphs.neptune_graph.NeptuneGraph¶
class langchain.graphs.neptune_graph.NeptuneGraph(host: str, port: int = 8182, use_https: bool = True, client: Any = None, credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, service: str = 'neptunedata', sign: bool = True)[source]¶
Neptune wra... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.neptune_graph.NeptuneGraph.html |
1a9522a35e73-1 | query(query[, params])
Query Neptune database.
__init__(host: str, port: int = 8182, use_https: bool = True, client: Any = None, credentials_profile_name: Optional[str] = None, region_name: Optional[str] = None, service: str = 'neptunedata', sign: bool = True) → None[source]¶
Create a new Neptune graph wrapper instance... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.neptune_graph.NeptuneGraph.html |
84654b042bf6-0 | langchain.graphs.networkx_graph.KnowledgeTriple¶
class langchain.graphs.networkx_graph.KnowledgeTriple(subject: str, predicate: str, object_: str)[source]¶
A triple in the graph.
Create new instance of KnowledgeTriple(subject, predicate, object_)
Attributes
object_
Alias for field number 2
predicate
Alias for field num... | lang/api.python.langchain.com/en/latest/graphs/langchain.graphs.networkx_graph.KnowledgeTriple.html |
c70b98bdb4cd-0 | langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain¶
class langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain[source]¶
Bases: Chain
Chain that interprets a prompt and executes python code to do symbolic math.
Example
from langchain.chains import LLMSymbolicMathChain
from langchain.llms im... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-1 | and passed as arguments to the handlers defined in callbacks.
You can use these to eg identify a specific instance of a chain with its use case.
param tags: Optional[List[str]] = None¶
Optional list of tags associated with the chain. Defaults to None.
These tags will be associated with each call to this chain,
and pass... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-2 | tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
metadata – Optional metadata associated with the chain. Defaults to None
include_run_info – Whether to include run ... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-3 | chain will be returned. Defaults to False.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-4 | with all the inputs
Parameters
*args – If the chain expects a single input, it can be passed in as the
sole positional argument.
callbacks – Callbacks to use for this chain run. These will be called in
addition to callbacks passed to the chain during construction, but only
these runtime callbacks will propagate to call... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-5 | Subclasses should override this method if they support streaming output.
async astream_log(input: Any, config: Optional[RunnableConfig] = None, *, diff: bool = True, include_names: Optional[Sequence[str]] = None, include_types: Optional[Sequence[str]] = None, include_tags: Optional[Sequence[str]] = None, exclude_names:... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-6 | e.g., if the underlying runnable uses an API which supports a batch mode.
bind(**kwargs: Any) → Runnable[Input, Output]¶
Bind arguments to a Runnable, returning a new Runnable.
config_schema(*, include: Optional[Sequence[str]] = None) → Type[BaseModel]¶
The type of config this runnable accepts specified as a pydantic m... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-7 | exclude – fields to exclude from new model, as with values this takes precedence over include
update – values to change/add in the new model. Note: the data is not validated before creating
the new model: you should trust this data
deep – set to True to make a deep copy of the model
Returns
new model instance
dict(**kw... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-8 | # -> {"_type": "foo", "verbose": False, ...}
classmethod from_llm(llm: BaseLanguageModel, prompt: BasePromptTemplate = PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s SymPy library. Use the output of running this code to answer the q... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-9 | classmethod from_orm(obj: Any) → Model¶
get_input_schema(config: Optional[RunnableConfig] = None) → Type[BaseModel]¶
Get a pydantic model that can be used to validate input to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic input schema that depend... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-10 | purposes, ‘max_concurrency’ for controlling how much work to do
in parallel, and other keys. Please refer to the RunnableConfig
for more details.
Returns
The output of the runnable.
classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]]... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-11 | prep_inputs(inputs: Union[Dict[str, Any], Any]) → Dict[str, str]¶
Validate and prepare chain inputs, including adding inputs from memory.
Parameters
inputs – Dictionary of raw inputs, or single input if chain expects
only one param. Should contain all inputs specified in
Chain.input_keys except for inputs that will be ... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-12 | these runtime callbacks will propagate to calls to other objects.
tags – List of string tags to pass to all callbacks. These will be passed in
addition to tags passed to the chain during construction, but only
these runtime tags will propagate to calls to other objects.
**kwargs – If the chain expects multiple inputs, ... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-13 | to_json() → Union[SerializedConstructor, SerializedNotImplemented]¶
to_json_not_implemented() → SerializedNotImplemented¶
transform(input: Iterator[Input], config: Optional[RunnableConfig] = None, **kwargs: Optional[Any]) → Iterator[Output]¶
Default implementation of transform, which buffers input and then calls stream... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-14 | on_error: Called if the runnable throws an error, with the Run object.
The Run object contains information about the run, including its id,
type, input, output, error, start_time, end_time, and any tags or metadata
added to the run.
with_retry(*, retry_if_exception_type: ~typing.Tuple[~typing.Type[BaseException], ...] ... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
c70b98bdb4cd-15 | These attributes must be accepted by the constructor.
property lc_secrets: Dict[str, str]¶
A map of constructor argument names to secret ids.
For example,{“openai_api_key”: “OPENAI_API_KEY”}
property output_schema: Type[pydantic.main.BaseModel]¶
The type of output this runnable produces specified as a pydantic model.
E... | lang/api.python.langchain.com/en/latest/llm_symbolic_math/langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.html |
ddd8366112fb-0 | langchain.cache.FullLLMCache¶
class langchain.cache.FullLLMCache(**kwargs)[source]¶
SQLite table for full LLM Cache (all generations).
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attributes o... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.FullLLMCache.html |
41363e5a12be-0 | langchain.cache.FullMd5LLMCache¶
class langchain.cache.FullMd5LLMCache(**kwargs)[source]¶
SQLite table for full LLM Cache (all generations).
A simple constructor that allows initialization from kwargs.
Sets attributes on the constructed instance using the names and
values in kwargs.
Only keys that are present as
attrib... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.FullMd5LLMCache.html |
454bc31da1b8-0 | langchain.cache.RedisSemanticCache¶
class langchain.cache.RedisSemanticCache(redis_url: str, embedding: Embeddings, score_threshold: float = 0.2)[source]¶
Cache that uses Redis as a vector-store backend.
Initialize by passing in the init GPTCache func
Parameters
redis_url (str) – URL to connect to Redis.
embedding (Emb... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.RedisSemanticCache.html |
454bc31da1b8-1 | set_llm_cache(RedisSemanticCache(
redis_url="redis://localhost:6379",
embedding=OpenAIEmbeddings()
))
clear(**kwargs: Any) → None[source]¶
Clear semantic cache for a given llm_string.
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][source]¶
Look up based on prompt and llm_string.
update(pr... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.RedisSemanticCache.html |
6b64fd0beea2-0 | langchain.cache.CassandraCache¶
class langchain.cache.CassandraCache(session: Optional[CassandraSession] = None, keyspace: Optional[str] = None, table_name: str = 'langchain_llm_cache', ttl_seconds: Optional[int] = None, skip_provisioning: bool = False)[source]¶
Cache that uses Cassandra / Astra DB as a backend.
It use... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.CassandraCache.html |
6b64fd0beea2-1 | A wrapper around delete with the LLM being passed.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_val)
Update cache based on prompt and llm_string.
__init__(session: Optional[CassandraSession] = None, keyspace: Optional[str] = None, table_name: str = 'langchain_llm_... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.CassandraCache.html |
602ded01a747-0 | langchain.cache.SQLAlchemyMd5Cache¶
class langchain.cache.SQLAlchemyMd5Cache(engine: ~sqlalchemy.engine.base.Engine, cache_schema: ~typing.Type[~langchain.cache.FullMd5LLMCache] = <class 'langchain.cache.FullMd5LLMCache'>)[source]¶
Cache that uses SQAlchemy as a backend.
Initialize by creating all tables.
Methods
__ini... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.SQLAlchemyMd5Cache.html |
25ae951a9068-0 | langchain.cache.MomentoCache¶
class langchain.cache.MomentoCache(cache_client: momento.CacheClient, cache_name: str, *, ttl: Optional[timedelta] = None, ensure_cache_exists: bool = True)[source]¶
Cache that uses Momento as a backend. See https://gomomento.com/
Instantiate a prompt cache using Momento as a backend.
Note... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.MomentoCache.html |
25ae951a9068-1 | Instantiate a prompt cache using Momento as a backend.
Note: to instantiate the cache client passed to MomentoCache,
you must have a Momento account. See https://gomomento.com/.
Parameters
cache_client (CacheClient) – The Momento cache client.
cache_name (str) – The name of the cache to use to store the data.
ttl (Opti... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.MomentoCache.html |
25ae951a9068-2 | Store llm generations in cache.
Parameters
prompt (str) – The prompt run through the language model.
llm_string (str) – The language model string.
return_val (RETURN_VAL_TYPE) – A list of language model generations.
Raises
SdkException – Momento service or network error
Exception – Unexpected response
Examples using Mo... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.MomentoCache.html |
887cf4117a62-0 | langchain.cache.GPTCache¶
class langchain.cache.GPTCache(init_func: Optional[Union[Callable[[Any, str], None], Callable[[Any], None]]] = None)[source]¶
Cache that uses GPTCache as a backend.
Initialize by passing in init function (default: None).
Parameters
init_func (Optional[Callable[[Any], None]]) – init GPTCache fu... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.GPTCache.html |
887cf4117a62-1 | # Initialize GPTCache with a custom init function
import gptcache
from gptcache.processor.pre import get_prompt
from gptcache.manager.factory import get_data_manager
from langchain.globals import set_llm_cache
# Avoid multiple caches using the same file,
causing different llm model caches to affect each other
def init_... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.GPTCache.html |
f9be7f5666ca-0 | langchain.cache.InMemoryCache¶
class langchain.cache.InMemoryCache[source]¶
Cache that stores things in memory.
Initialize with empty cache.
Methods
__init__()
Initialize with empty cache.
clear(**kwargs)
Clear cache.
lookup(prompt, llm_string)
Look up based on prompt and llm_string.
update(prompt, llm_string, return_v... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.InMemoryCache.html |
d303a8a6e5c3-0 | langchain.cache.SQLiteCache¶
class langchain.cache.SQLiteCache(database_path: str = '.langchain.db')[source]¶
Cache that uses SQLite as a backend.
Initialize by creating the engine and all tables.
Methods
__init__([database_path])
Initialize by creating the engine and all tables.
clear(**kwargs)
Clear cache.
lookup(pro... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.SQLiteCache.html |
0042dc44a83b-0 | langchain.cache.RedisCache¶
class langchain.cache.RedisCache(redis_: Any, *, ttl: Optional[int] = None)[source]¶
Cache that uses Redis as a backend.
Initialize an instance of RedisCache.
This method initializes an object with Redis caching capabilities.
It takes a redis_ parameter, which should be an instance of a Redi... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.RedisCache.html |
0042dc44a83b-1 | If provided, it sets the time duration for how long cached
items will remain valid. If not provided, cached items will not
have an automatic expiration.
clear(**kwargs: Any) → None[source]¶
Clear cache. If asynchronous is True, flush asynchronously.
lookup(prompt: str, llm_string: str) → Optional[Sequence[Generation]][... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.RedisCache.html |
a84e5779f7af-0 | langchain.cache.UpstashRedisCache¶
class langchain.cache.UpstashRedisCache(redis_: Any, *, ttl: Optional[int] = None)[source]¶
Cache that uses Upstash Redis as a backend.
Initialize an instance of UpstashRedisCache.
This method initializes an object with Upstash Redis caching capabilities.
It takes a redis_ parameter, ... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.UpstashRedisCache.html |
a84e5779f7af-1 | This allows the object to communicate with
Redis server for caching operations on.
ttl (int, optional) – Time-to-live (TTL) for cached items in seconds.
If provided, it sets the time duration for how long cached
items will remain valid. If not provided, cached items will not
have an automatic expiration.
clear(**kwargs... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.UpstashRedisCache.html |
95b051bd9b2e-0 | langchain.cache.SQLAlchemyCache¶
class langchain.cache.SQLAlchemyCache(engine: ~sqlalchemy.engine.base.Engine, cache_schema: ~typing.Type[~langchain.cache.FullLLMCache] = <class 'langchain.cache.FullLLMCache'>)[source]¶
Cache that uses SQAlchemy as a backend.
Initialize by creating all tables.
Methods
__init__(engine[,... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.SQLAlchemyCache.html |
86502af505b3-0 | langchain.cache.CassandraSemanticCache¶
class langchain.cache.CassandraSemanticCache(session: Optional[CassandraSession], keyspace: Optional[str], embedding: Embeddings, table_name: str = 'langchain_llm_semantic_cache', distance_metric: str = 'dot', score_threshold: float = 0.85, ttl_seconds: Optional[int] = None, skip... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.CassandraSemanticCache.html |
86502af505b3-1 | Methods
__init__(session, keyspace, embedding[, ...])
Initialize the cache with all relevant parameters. :param session: an open Cassandra session :type session: cassandra.cluster.Session :param keyspace: the keyspace to use for storing the cache :type keyspace: str :param embedding: Embedding provider for semantic ... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.CassandraSemanticCache.html |
86502af505b3-2 | Initialize the cache with all relevant parameters.
:param session: an open Cassandra session
:type session: cassandra.cluster.Session
:param keyspace: the keyspace to use for storing the cache
:type keyspace: str
:param embedding: Embedding provider for semantic
encoding and search.
Parameters
table_name (str) – name o... | lang/api.python.langchain.com/en/latest/cache/langchain.cache.CassandraSemanticCache.html |
86502af505b3-3 | Update cache based on prompt and llm_string. | lang/api.python.langchain.com/en/latest/cache/langchain.cache.CassandraSemanticCache.html |
ee177465870a-0 | langchain.llms.base.get_prompts¶
langchain.llms.base.get_prompts(params: Dict[str, Any], prompts: List[str]) → Tuple[Dict[int, List], str, List[int], List[str]][source]¶
Get prompts that are already cached. | lang/api.python.langchain.com/en/latest/llms/langchain.llms.base.get_prompts.html |
1f2fea10ccf9-0 | langchain.llms.beam.Beam¶
class langchain.llms.beam.Beam[source]¶
Bases: LLM
Beam API for gpt2 large language model.
To use, you should have the beam-sdk python package installed,
and the environment variable BEAM_CLIENT_ID set with your client id
and BEAM_CLIENT_SECRET set with your client secret. Information on how
t... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-1 | param gpu: str = ''¶
param max_length: str = ''¶
param memory: str = ''¶
param metadata: Optional[Dict[str, Any]] = None¶
Metadata to add to the run trace.
param model_kwargs: Dict[str, Any] [Optional]¶
Holds any model parameters valid for create call not
explicitly specified.
param model_name: str = ''¶
param name: st... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-2 | e.g., if the underlying runnable uses an API which supports a batch mode.
async agenerate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[Li... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-3 | functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
async a... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-4 | Parameters
messages – A sequence of chat messages corresponding to a single model input.
stop – Stop words to use when generating. Model output is cut off at the
first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Retur... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-5 | Default implementation of atransform, which buffers input and calls astream.
Subclasses should override this method if they can start producing output while
input is still being generated.
batch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = Non... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-6 | Default values are respected, but no other validation is performed.
Behaves as if Config.extra = ‘allow’ was set since it adds all passed values
copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-7 | Run the LLM on the given prompt and input.
generate_prompt(prompts: List[PromptValue], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, **kwargs: Any) → LLMResult¶
Pass a sequence of pr... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-8 | This method allows to get an input schema for a specific configuration.
Parameters
config – A config to use when generating the schema.
Returns
A pydantic model that can be used to validate input.
classmethod get_lc_namespace() → List[str]¶
Get the namespace of the langchain object.
For example, if the class is langcha... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-9 | Parameters
text – The string input to tokenize.
Returns
A list of ids corresponding to the tokens in the text, in order they occurin the text.
invoke(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → str¶
Transform a single ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-10 | to the object.
map() → Runnable[List[Input], List[Output]]¶
Return a new Runnable that maps a list of inputs to a list of outputs,
by calling invoke() with each input.
classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool =... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-11 | first occurrence of any of these substrings.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a message.
run_creation() → None[source]¶
Creates a Python file which will be deployed on beam.
save(file_path: Union[Path, str]) → Non... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-12 | classmethod validate(value: Any) → Model¶
with_config(config: Optional[RunnableConfig] = None, **kwargs: Any) → Runnable[Input, Output]¶
Bind config to a Runnable, returning a new Runnable.
with_fallbacks(fallbacks: Sequence[Runnable[Input, Output]], *, exceptions_to_handle: Tuple[Type[BaseException], ...] = (<class 'E... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
1f2fea10ccf9-13 | Create a new Runnable that retries the original runnable on exceptions.
Parameters
retry_if_exception_type – A tuple of exception types to retry on
wait_exponential_jitter – Whether to add jitter to the wait time
between retries
stop_after_attempt – The maximum number of attempts to make before giving up
Returns
A new ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.beam.Beam.html |
b0652f76899c-0 | langchain.llms.cohere.completion_with_retry¶
langchain.llms.cohere.completion_with_retry(llm: Cohere, **kwargs: Any) → Any[source]¶
Use tenacity to retry the completion call. | lang/api.python.langchain.com/en/latest/llms/langchain.llms.cohere.completion_with_retry.html |
2087e80acaa0-0 | langchain.llms.amazon_api_gateway.AmazonAPIGateway¶
class langchain.llms.amazon_api_gateway.AmazonAPIGateway[source]¶
Bases: LLM
Amazon API Gateway to access LLM models hosted on AWS.
Create a new model by parsing and validating input data from keyword arguments.
Raises ValidationError if the input data cannot be parse... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-1 | Check Cache and run the LLM on the given prompt and input.
async abatch(inputs: List[Union[PromptValue, str, List[BaseMessage]]], config: Optional[Union[RunnableConfig, List[RunnableConfig]]] = None, *, return_exceptions: bool = False, **kwargs: Any) → List[str]¶
Default implementation runs ainvoke in parallel using as... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-2 | need more output from the model than just the top generated value,
are building chains that are agnostic to the underlying language modeltype (e.g., pure text completion models vs chat models).
Parameters
prompts – List of PromptValues. A PromptValue is an object that can be
converted to match the format of any languag... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-3 | **kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
Top model prediction as a string.
async apredict_messages(messages: List[BaseMessage], *, stop: Optional[Sequence[str]] = None, **kwargs: Any) → BaseMessage¶
Asynchronously pass messages to the model and ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-4 | This includes all inner runs of LLMs, Retrievers, Tools, etc.
Output is streamed as Log objects, which include a list of
jsonpatch ops that describe how the state of the run has changed in each
step, and the final state of the run.
The jsonpatch ops can be applied in order to construct state.
async atransform(input: As... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-5 | Returns
A pydantic model that can be used to validate config.
configurable_alternatives(which: ConfigurableField, default_key: str = 'default', **kwargs: Union[Runnable[Input, Output], Callable[[], Runnable[Input, Output]]]) → RunnableSerializable[Input, Output]¶
configurable_fields(**kwargs: Union[ConfigurableField, C... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-6 | classmethod from_orm(obj: Any) → Model¶
generate(prompts: List[str], stop: Optional[List[str]] = None, callbacks: Union[List[BaseCallbackHandler], BaseCallbackManager, None, List[Optional[Union[List[BaseCallbackHandler], BaseCallbackManager]]]] = None, *, tags: Optional[Union[List[str], List[List[str]]]] = None, metada... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-7 | functionality, such as logging or streaming, throughout generation.
**kwargs – Arbitrary additional keyword arguments. These are usually passed
to the model provider API call.
Returns
An LLMResult, which contains a list of candidate Generations for each inputprompt and additional model provider-specific output.
get_inp... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-8 | Get a pydantic model that can be used to validate output to the runnable.
Runnables that leverage the configurable_fields and configurable_alternatives
methods will have a dynamic output schema that depends on which
configuration the runnable is invoked with.
This method allows to get an output schema for a specific co... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-9 | classmethod is_lc_serializable() → bool¶
Is this class serializable?
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defa... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-10 | Pass a single string input to the model and return a string prediction.
Use this method when passing in raw text. If you want to pass in specifictypes of chat messages, use predict_messages.
Parameters
text – String input to pass to the model.
stop – Stop words to use when generating. Model output is cut off at the
fir... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-11 | stream(input: Union[PromptValue, str, List[BaseMessage]], config: Optional[RunnableConfig] = None, *, stop: Optional[List[str]] = None, **kwargs: Any) → Iterator[str]¶
Default implementation of stream, which calls invoke.
Subclasses should override this method if they support streaming output.
to_json() → Union[Seriali... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
2087e80acaa0-12 | fallback in order, upon failures.
with_listeners(*, on_start: Optional[Listener] = None, on_end: Optional[Listener] = None, on_error: Optional[Listener] = None) → Runnable[Input, Output]¶
Bind lifecycle listeners to a Runnable, returning a new Runnable.
on_start: Called before the runnable starts running, with the Run ... | lang/api.python.langchain.com/en/latest/llms/langchain.llms.amazon_api_gateway.AmazonAPIGateway.html |
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